Clustering-Based Denoising With Locally Learned Dictionaries

By Priyam Chatterjee, Peyman Milanfar. IEEE Transactions on Image Processing, 2009

329 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/13ac619b13522b2c0b3564d751ad5ea56360c81f

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Citations in the corpus

Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections (2016, unknown): “7 5.2 Image denoising . . . . . . . . . . . . . . 9 5.3 Image super-resolution . . . . . . . . . . 9 5.4 JPEG deblocking . . . . . . . . . . . . . 12 5.5 Image inpainting . . . . . . . . . . . . . 12”
Denoising of 3D magnetic resonance images by using higher-order singular value decomposition (2015, unknown): “The framework of grouping similar patches has the advantage of enhancing sparsity and is the reason behind the outstanding performance of BM3D and also adopted in many recent papers (Chatterjee and Milanfar, 2009; Dong et al., 2013; Mairal et al., 2009).”
Denoising of 3 D Magnetic Resonance Images by Using 1 Higher-Order Singular Value Decomposition 2 (2014, methodology): “Noise estimation and removal in MR imaging: The 13 variance-stabilization approach, Biomedical Imaging: From Nano to Macro, 14”
Sparsity-based image denoising via dictionary learning and structural clustering (2011, background): “, nonlocal mean [4], BM3D [5], locally learned dictionaries K-LLD [6], learned simultaneous sparse coding (LSSC) [7].”
Centralized sparse representation for image restoration (2011, methodology): “Such a local PCA strategy has been used in [28, 8, 15, 14] for image reconstruction.”
Image Denoising Using the Higher Order Singular Value Decomposition (2013, unknown): “As against learning a single overcomplete dictionary for the entire image, the authors of the KLLD (K locally learned dictionaries) approach [6] perform a clustering step using K-Means on (coarsely prefiltered) patches from the noisy image and then filter the patches from each cluster separately by…”
BM3D Image Denoising with Shape-Adaptive Principal Component Analysis (2009, background): “…learned dictionaries of atoms to (cid:222) lter small square neighborhoods [6]; steering kernel regression [16] (also combined with learned dictionaries [2]); shape-adaptive DCT (SA-DCT) on neighborhoods whose shapes are adaptive to the image structures [7]; nonlocal (cid:222) ltering based on…”
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections (2016, methodology): “Traditional methods such as Total variation [24, 23], BM3D algorithm [5] and dictionary learning based methods [31, 10, 2] have shown very good performance on image restoration topics such as image denoising and super-resolution.”
Single-Image Noise Level Estimation for Blind Denoising (2013, background): “Many efficient non-blind denoising algorithms have appeared [14]–[18] in recent years.”
From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms (2014, methodology): “Rather than learning a single dictionary for the entire image in KSVD, classification based on the SKR is exploited in order to avoid patches with different structures being considered similar patches in locally learned dictionaries (K-LLD) [72].”